Systems and methods for predicting coronary plaque vulnerability from patient-specific anatomic image data

ABSTRACT

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient&#39;s vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient&#39;s vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.61/917,639 filed Dec. 18, 2013, the entire disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods forpredicting coronary plaque vulnerability from patient-specific anatomicimage data.

BACKGROUND

Coronary artery disease may produce coronary lesions in the bloodvessels providing blood to the heart, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack.

Patients suffering from chest pain and/or exhibiting symptoms ofcoronary artery disease may be subjected to one or more tests that mayprovide some indirect evidence relating to coronary lesions. Forexample, noninvasive tests may include electrocardiograms, biomarkerevaluation from blood tests, treadmill tests, echocardiography, singlepositron emission computed tomography (SPECT), and positron emissiontomography (PET). Anatomic data may be obtained noninvasively usingcoronary computed tomographic angiography (CCTA). CCTA may be used forimaging of patients with chest pain and involves using computedtomography (CT) technology to image the heart and the coronary arteriesfollowing an intravenous infusion of a contrast agent.

Meanwhile, vulnerable plaque features, such as adverse plaquecharacteristics (APCs)), have been actively investigated for prognosisof major adverse cardiac events (MACE) using both invasive andnoninvasive techniques, such as intravascular ultrasound (IVUS), opticalcoherence tomography (OCT), and coronary computed tomography data(CCTA).

However, a need exists for systems and methods for predicting coronaryplaque vulnerability from patient-specific anatomic image data.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for predicting coronary plaque vulnerability frompatient-specific anatomic image data. One method includes: acquiringanatomical image data of at least part of the patient's vascular system;performing, using a processor, one or more image characteristicsanalysis, geometrical analysis, computational fluid dynamics analysis,and structural mechanics analysis on the anatomical image data;predicting, using the processor, a coronary plaque vulnerability presentin the patient's vascular system, wherein predicting the coronary plaquevulnerability includes calculating an adverse plaque characteristicbased on results of the one or more image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis of the anatomical image data; andreporting, using the processor, the calculated adverse plaquecharacteristic.

In accordance with another embodiment, a system for reporting coronaryplaque vulnerability from patient-specific anatomic image data,comprises: a data storage device storing instructions for predictingcoronary plaque vulnerability from patient-specific anatomic image data;and a processor configured for: acquiring anatomical image data of atleast part of the patient's vascular system; performing, using aprocessor, one or more image characteristics analysis, geometricalanalysis, computational fluid dynamics analysis, and structuralmechanics analysis on the anatomical image data; predicting, using theprocessor, a coronary plaque vulnerability present in the patient'svascular system, wherein predicting the coronary plaque vulnerabilityincludes calculating an adverse plaque characteristic based on resultsof the one or more image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysisof the anatomical image data; and reporting, using the processor, thecalculated adverse plaque characteristic.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofreporting coronary plaque vulnerability from patient-specific anatomicimage data is provided. The method includes: acquiring anatomical imagedata of at least part of the patient's vascular system; performing,using a processor, one or more image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis on the anatomical image data; predicting,using the processor, a coronary plaque vulnerability present in thepatient's vascular system, wherein predicting the coronary plaquevulnerability includes calculating an adverse plaque characteristicbased on results of the one or more image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis of the anatomical image data; andreporting, using the processor, the calculated adverse plaquecharacteristic.

Another method includes: acquiring anatomical image data of at leastpart of the patient's vascular system; performing, using a processor,one or more image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image data; and predicting, using the processor, aprobability of an adverse cardiac event from coronary plaquevulnerability present in the patient's vascular system based on resultsof the one or more image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysisof the anatomical image data.

In accordance with another embodiment, a system of predicting coronaryplaque vulnerability from patient-specific anatomic image data,comprises: a data storage device storing instructions for predictingcoronary plaque vulnerability from patient-specific anatomic image data;and a processor configured to: to execute the instructions to perform amethod including: acquiring anatomical image data of at least part ofthe patient's vascular system; performing, using a processor, one ormore image characteristics analysis, geometrical analysis, computationalfluid dynamics analysis, and structural mechanics analysis on theanatomical image data; and predicting, using the processor, aprobability of an adverse cardiac event coronary plaque vulnerabilitypresent in the patient's vascular system, based on results of the one ormore image characteristics analysis, geometrical analysis, computationalfluid dynamics analysis, and structural mechanics analysis of theanatomical image data.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for predicting coronaryplaque vulnerability from patient-specific anatomic image data isprovided. The method includes: acquiring anatomical image data of atleast part of the patient's vascular system; performing, using aprocessor, one or more image characteristics analysis, geometricalanalysis, computational fluid dynamics analysis, and structuralmechanics analysis on the anatomical image data; and predicting, usingthe processor, a probability of an adverse cardiac event coronary plaquevulnerability present in the patient's vascular system, based on resultsof the one or more image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysisof the anatomical image data.

Yet another method includes: acquiring anatomical image data of at leastpart of a patient's vascular system; performing, using a processor, oneor more of image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image data; predicting, using the processor, acoronary plaque vulnerability present in the patient's vascular systembased on results of one or more of the image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis of the anatomical image data; modifyingone or more of the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image data based on a proposed treatment; anddetermining an effect of the treatment on the prediction of the coronaryplaque vulnerability based on the modified one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage.

In accordance with another embodiment, a system of determining theeffect of a treatment on coronary plaque vulnerability, comprises: adata storage device storing instructions for predicting coronary plaquevulnerability from patient-specific anatomic image data; and a processorconfigured to execute the instructions to perform a method including:acquiring anatomical image data of at least part of the patient'svascular system; performing, using a processor, one or more of imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage data; predicting, using the processor, a coronary plaquevulnerability present in the patient's vascular system based on resultsof one or more of the image characteristics analysis, geometricalanalysis, computational fluid dynamics analysis, and structuralmechanics analysis of the anatomical image data; modifying one or moreof the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image data based on a proposed treatment; anddetermining an effect of the treatment on the prediction of the coronaryplaque vulnerability based on the modified one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage.

In accordance with yet another embodiment, a non-transitory computerreadable medium for use on a computer system containingcomputer-executable programming instructions for performing a method ofdetermining the effect of a treatment on coronary plaque vulnerabilityis provided. The method comprises: acquiring anatomical image data of atleast part of the patient's vascular system; performing, using aprocessor, one or more of image characteristics analysis, geometricalanalysis, computational fluid dynamics analysis, and structuralmechanics analysis on the anatomical image data; predicting, using theprocessor, a coronary plaque vulnerability present in the patient'svascular system based on results of one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis of the anatomicalimage data; modifying one or more of the image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis on the anatomical image data based on aproposed treatment; and determining an effect of the treatment on theprediction of the coronary plaque vulnerability based on the modifiedone or more of the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forpredicting coronary plaque vulnerability from patient-specific anatomicimage data, according to an exemplary embodiment of the presentdisclosure.

FIG. 2 is a block diagram of an exemplary method for predicting coronaryplaque vulnerability from patient-specific anatomic image data,according to an exemplary embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary method for reporting adverseplaque characteristics from patient-specific anatomic image data,according to an exemplary embodiment of the present disclosure.

FIG. 4A is a block diagram of an exemplary method for predicting cardiacrisk or risk-related features from patient-specific anatomic image data,according to an exemplary embodiment of the present disclosure.

FIG. 4B is a block diagram of an exemplary method for creating andtraining a prediction system to predict cardiac risk or risk-relatedfeatures from patient-specific anatomic image data, according to anexemplary embodiment of the present disclosure.

FIG. 5A is a block diagram of an exemplary method for predicting changeof cardiac risk or risk-related features in response to medicaltreatment protocols from patient-specific anatomic image data, accordingto an exemplary embodiment of the present disclosure.

FIG. 5B is a block diagram of an exemplary method for creating andtraining a prediction system to predict, using patient-specific anatomicimage data, change of cardiac risk or risk-related features in responseto medical treatment protocols and/or lifestyle modifications, accordingto an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

As described above, a new generation of noninvasive tests have beendeveloped to assess blood flow characteristics. These noninvasive testsuse patient imaging (such as CT) to determine a patient-specificgeometric model of blood vessels, which may be used computationally tosimulate blood flow using computational fluid dynamics (CFD) along withappropriate physiological boundary conditions and parameters. Examplesof inputs to these patient-specific boundary conditions include thepatient's blood pressure, blood viscosity, and the expected demand ofblood from supplied tissue (derived from scaling laws and a massestimation of the supplied tissue from the patient imaging).

The present disclosure is directed to a new approach for providingprognosis of adverse cardiac events and for guiding medical therapybased on patient-specific geometry and blood flow characteristics.Although the present disclosure is described with respect to coronaryartery disease, the same system is applicable to creating apatient-specific prediction of rupture risks in other vascular systemsbeyond the coronary arteries, such as the carotid artery.

More specifically, the present disclosure is directed to using patients'cardiac imaging to derive a patient-specific geometric model of thecoronary vessels. Coronary flow simulations with respect to patientphysiological information and estimated boundary conditions may then beperformed on the model to extract hemodynamic characteristics. Thehemodynamic characteristics may be used to predict cardiac events,including plaque rupture and/or myocardial infarction. The presentdisclosure may use physics-based simulation of blood flow to predictthose cardiac events. In addition, the present disclosure includes theuse of machine learning or rule-based methods to achieve thepredictions. Furthermore, the machine-learning and rule-based methodsmay incorporate various risk factors, including patient demographics,biomarkers, and/or coronary geometry, as well as the results ofpatient-specific biophysical simulations (e.g., hemodynamiccharacteristics). If additional diagnostic test results are available,those results can be used to train a machine-learning algorithm, forexample, in making a prediction. Several predictions may be made basedon the processing described. Specifically, the present disclosureprovides a system and method for prediction and/or report of: (i)adverse plaque characteristics; (ii) cardiac risk (or cardiacrisk-related features); and (iii) change of risk factors in response tovarious medical treatment protocols to guide medical therapy planning.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for predicting coronary plaquevulnerability from patient-specific anatomic image data. Specifically,FIG. 1 depicts a plurality of physicians 102 and third party providers104, any of whom may be connected to an electronic network 100, such asthe Internet, through one or more computers, servers, and/or handheldmobile devices. Physicians 102 and/or third party providers 104 maycreate or otherwise obtain images of one or more patients' cardiacand/or vascular systems. The physicians 102 and/or third party providers104 may also obtain any combination of patient-specific information,such as age, medical history, blood pressure, blood viscosity, etc.Physicians 102 and/or third party providers 104 may transmit thecardiac/vascular images and/or patient-specific information to serversystems 106 over the electronic network 100. Server systems 106 mayinclude storage devices for storing images and data received fromphysicians 102 and/or third party providers 104. Server systems 106 mayalso include processing devices for processing images and data stored inthe storage devices.

FIG. 2 is a block diagram of an exemplary method 200 for predictingcoronary plaque vulnerability from patient-specific anatomic image data,according to an exemplary embodiment of the present disclosure. Method200 may be performed by server systems 106, based on information,images, and data received from physicians 102 and/or third partyproviders 104 over electronic network 100. The method of FIG. 2 mayinclude acquiring a model of coronary geometry and performing analysisusing the model in order to predict plaque vulnerability and drawconclusions based on those predictions. As shown in FIG. 2, in general,method 200 may include obtaining patient-specific information (e.g., CTscan images, phenotype information, etc.) (step 202), constructing apatient-specific geometric model (step 204), performing flow dynamicsand structural mechanics simulations on geometrical features and imagefeatures of the model, and extracting hemodynamic and mechanicalcharacteristics (step 206). Based on the extracted characteristics andfeatures, the server systems 106 may then perform step 208 to predictand/or report adverse plaque characteristics (APCs) (step 210). Furtherdetail of one embodiment of step 210 is provided in FIG. 3, in whichmetrics for computing APCs are determined, and metric values are foundfor specific patients, in order to determine APCs associated with thepatients.

In another embodiment, performing step 208 may cause server systems 106to further predict and/or report cardiac risk or cardiac risk-relatedfeatures (e.g., predicting plaque rupture or occurrence of myocardialinfarction) (step 212). For example, FIGS. 4A and 4B describe oneembodiment of step 212 in more detail, in which feature vectors arecreated for points in the patient-specific geometric model andprobability of plaque rupture or MI event is estimated by analyzingfeature weights. In yet another embodiment, server systems 106 maypredict and/or report optimal treatment protocols in response to therisk (step 214). For example, FIGS. 5A and 5B provide more detail on oneembodiment of step 214 by describing how to find the impact of variousmedical therapy protocols and/or lifestyle modifications on risk factorprediction.

Thus, in one embodiment, method 200 may employ a patient-specific modelof coronary geometry to predict and report one or more of APCs, cardiacrisk, and/or treatment. Method 200 may include obtaining apatient-specific geometric model (step 202) comprising a digitalrepresentation (e.g., the memory or digital storage (including a harddrive and/or network drive) of a computational device such as acomputer, laptop, DSP, server, etc.). The coronary geometry may berepresented as a list of points in space, possibly with a list ofneighbors for each point, in which the space can be mapped to spatialunits between points (e.g., millimeters).

In one embodiment, step 202 may comprise obtaining the model, such as byconstructing a patient-specific model of coronary geometry, forinstance, by modeling a patient's coronary vasculature, including one ormore lumens, plaque, and/or lumen walls (step 204). Given a 3-D image ofcoronary vasculature, many methods exist for extracting a model ofcardiovascular geometry pertaining to a specific patient. Thepatient-specific model may be constructed or rendered based on images,such as CT scans associated with a patient. In one embodiment, the modelmay be derived by performing a cardiac CT in the end of a diastole phaseof the cardiac cycle, for instance, using Black-Blood Magnetic ResonanceImaging. The image may be segmented manually or automatically toidentity voxels belonging to areas of interest. Inaccuracies in thegeometry may be extracted automatically and optionally corrected by ahuman observer. For instance, a human observer may compare the extractedgeometry with the CT images and make corrections as needed. Once voxelsare identified, the geometric model can be derived (e.g., using marchingcubes). Step 204 may include all the components necessary to construct apatient-specific model.

Once a model is available, step 206 may include performing variousphysics-based simulations on the model to derive conclusions relating tocoronary plaque vulnerability. Such conclusions may include, forexample, predicting and reporting on cardiac risk and proposedtreatment. In one embodiment, method 200 may employ machine learning orrule-based methods that incorporate various risk factors, includingpatient demographics, biomarkers, coronary geometry, as well as theresults of patient-specific biophysical simulations (e.g., hemodynamiccharacteristics). Additional diagnostic test results may also be used totrain the machine learning algorithms for better predictions. Step 208may then use results from step 206 to predict and/or report on (1)adverse plaque characteristics (APCs) (step 210), (2) cardiac risk orcardiac risk-related features (e.g., predicting plaque rupture oroccurrence of myocardial infarction) (step 212), and/or (3) optimaltreatment protocols in response to the risk (step 214).

FIG. 3 is a block diagram of an exemplary method 300 for reportingadverse plaque characteristics (APCs) from a patient-specific model. Themethod of FIG. 3 may be performed by server systems 106, based oninformation, images, and/or data received from physicians 102 and/orthird party providers 104 over electronic network 100. In oneembodiment, method 300 may be performed using a patient-specific modelof a patient's coronary vasculature. For example, the patient-specificmodel may include the geometry for, at least, the patient's coronaryartery tree, including the lumen, plaque, and/or lumen walls (i.e.,external elastic membrane (EEM) of the coronary arteries). The model maybe segmented manually or automatically to identify voxels belonging tothe lumen and lumen wall. Wall segmentation may include calcified andnon-calcified plaques. In analyzing the model to report adverse plaquecharacteristics, method 300 may include determining or defining metricsfor computing APCs (step 302). Exemplary metrics include: the presenceof positive remodeling, low attenuation plaque, spotty intra-plaquecalcification, etc. Step 302 may further include determining additionalmetrics for computation of APCs or prioritizing which metrics to use forcomputing APCs. Prioritizing metrics in step 302 may be used, forinstance, where computational capacity is limited or where timeconstraints may not permit computing all the metrics. Step 302 mayoptionally involve computing other risk factors.

Based on the metrics determined in step 302, method 300 may next includecalculating values for the metrics (step 304). For instance, step 304may include executing computations to find control or threshold values,as well as patient-specific values for the metrics. For the constructedlumen and wall geometries of the patient, method 300 may then includeautomatically calculating values for each metric for use in computingAPCs for the constructed lumen and wall geometries of the patient.

For the exemplary metrics, the presence of positive remodeling, thepresence of low attenuation plaque, and/or the presence of spottycalcification, step 304 may proceed according to the followingdescription. For example for the presence of positive remodeling metric,step 304 may include, first, detecting stenosis or presence of plaque ina wall segmentation. A segment may be identified as diseased based onthe degree of stenosis or amount of plaque. Next, step 304 may includecomputing a positive remodeling index, for example, by evaluating across-sectional area (CSA) of EEM at a lesion and reference CSA based onthe following equation:

${{Positive}\mspace{14mu} {remodeling}\mspace{14mu} {index}} = \frac{{CSA}\mspace{14mu} {of}\mspace{14mu} {EEM}\mspace{14mu} {at}\mspace{14mu} {lesion}}{{CSA}\mspace{14mu} {of}\mspace{14mu} {EEM}\mspace{14mu} {at}\mspace{14mu} {reference}}$

In one embodiment, the threshold value for a positive remodeling indexto indicate the presence of positive remodeling is 1.05. In other words,if the computed, patient positive remodeling index >1.05, step 304 mayinclude reporting that positive remodeling is present. Step 304 may theninclude reporting that there is, in fact, presence of positiveremodeling detected and/or the positive remodeling index. This metric ofthe positive remodeling index may factor into calculation of APCs. Thecalculation of APCs may also include determining the presence of lowattenuation plaque, for instance, by detecting non-calcified plaques inwall segmentation at a diseased segment. For example, if there exists aregion of non-calcified plaque whose intensity is ≦30 Hounsfield Unit(HU), step 304 may include reporting the presence of low attenuationplaque as true and/or the volume of the non-calcified plaque whoseintensity is ≦30 HUs.

The calculation of APCs may further include determining the presence ofspotty intra-plaque calcification (e.g., using image characteristicsanalysis to find spotty calcification), such as by detecting calcifiedplaques in wall segmentation at a diseased segment. Hessian-basedeigenvalue analysis may be utilized to detect blob-shaped calcifiedplaques. If the diameter of intra-lesion nodular calcified plaque 3 mm,then method 300 may include reporting the presence of spottycalcification as true and/or reporting the diameter.

Based on the calculated metrics, step 306 may calculate APCs. Eachmetric may alone constitute an APC, or the metrics may be combined in aform indicative of collective APCs. Step 306 may optionally involvecalculating other risk factors.

Finally, method 300 may include step 308 of saving the results ofcomputed APCs scores and/or other risk factors with images as a digitalrepresentation (e.g., the memory or digital storage (e.g., hard drive,network drive) of a computational device such as a computer, laptop,DSP, server, etc.) and making them available to a physician, forinstance. In one embodiment, step 308 may include actively reportingAPCs and/or other risk factors to physicians. In another embodiment,step 308 may simply prompt or signal to a user that computed APC scoresand risk factors are available for viewing and/or verification.

FIG. 4A is a block diagram of an exemplary method 400 for predictingcardiac risk or risk-related features based on patient-specific models.The method of FIG. 4A may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 100. Method 400 may beperformed on a patient-specific model including one or more modeledlumens, plaque, lumen walls, left and right myocardium, etc. Forinstance, the model may describe a patient's ascending aorta, coronaryartery tree, myocardium, valves, and chambers. Then, segmenting may helpidentify voxels belonging to the aorta and the lumen of the coronaryarteries.

In one embodiment, method 400 may include constructing the model fromthe patient image(s) prior to assessing the model for cardiac risk.Furthermore, method 400 may include collecting information, includingpatient demographics (e.g., age, gender, weight, blood pressure, etc.)and/or biomarkers (e.g., blood markers, DNA sequencing, etc.). Thispatient information may further inform construction of thepatient-specific model.

Once an appropriate patient-specific model is obtained, method 400 mayinclude extracting various features from the model (step 402). As shownin FIG. 4A, step 402 may include extracting geometrical features, imagefeatures, hemodynamic features, and/or biomechanical features (of vesselwalls and plaque). Image features may be extracted by computing coronaryand plaque characteristics and by computing anatomical characteristics.Computed coronary and plaque characteristics may include: APCs, plaqueburden (thickness, area, volume, etc.), SYNTAX score, napkin ring,necrotic core, lumen narrowing, minimum lumen diameter (MLD), minimumlumen area (MLA), percentage diameter stenosis, and/or percentage areastenosis. Computed anatomical characteristics may include: epicardialfat volume and/or myocardium shape.

Hemodynamic features may be extracted, for instance, by performingcomputational flow dynamic analysis for various physiologic conditions(e.g., rest, exercise, hyperemia, etc.) and/or computing hemodynamiccharacteristics associated with lesions (e.g., max/mean/cyclic wallshear stress, traction, turbulent kinetic energy, etc.). Extractingbiomechanical features of vessel wall(s) and plaque may include definingbiomechanical properties of vessel wall and plaques based on geometricaland image features (e.g., vessel wall density and elastic propertiesusing linear or nonlinear elasticity model; plaque density and elasticproperties using linear or nonlinear elasticity model; and/or ultimatestrength of plaque). Using the extracted features, method 400 mayinclude performing computational solid dynamic analysis for variousphysiologic conditions under steady and/or pulsatile flow (e.g., forrest, exercise, hyperemia, etc.). Method 400 may also include computingtissue stress and strain characteristics in lesions (e.g.,max/mean/cyclic stress, ultimate stress, turbulent kinetic energy, etc.)and/or generating a Goodman diagram to identify plaque rupture riskbased on mean and alternating stresses. In doing so, step 404 mayinclude creating a feature vector for every point in thepatient-specific geometric model, comprising a numerical description ofthe geometry, biophysical hemodynamic, and wall and plaque biomechanicalcharacteristic at that point, as well as estimates of physiological orphenotypic parameters of the patient. Alternately or in addition, step404 may include determining every location in the patient-specificgeometric model for which plaque vulnerability may be identified,wherein a feature vector is created only for such locations.

Then, step 406 may include producing estimates of cardiac risk,including estimates of the probability of plaque rupture or probabilityof the event of myocardial infarction at lesions in the patient-specificgeometric model. In one embodiment, the estimates are produced using amachine learning technique described in further detail in FIG. 4B. Forinstance, a prediction system may employ machine-learning techniques tohelp produce a vulnerability score for one or more locations of coronarylesions. The calculated vulnerability scores may be an application ofthe machine learning technique in a production mode, separate from atraining mode where the machine learning technique processes numerouspatient-specific models to develop the ability to make predictions for atarget patient.

Finally, method 400 may include step 408 where the estimates arereported to physicians, for instance, in the form of cardiac risk. Thecardiac risk discussed including risk of plaque rupture, possibility ofan MI event, etc. are merely exemplary instances of cardiac risk. Method400 may be applied to predicting and reporting any measurement ofcardiac risk.

FIG. 4B is a block diagram of an exemplary method 420 for creating andtraining a prediction system to predict cardiac risk. In one embodiment,the prediction system trained via method 420 may permit the estimates ofcardiac risk for method 400. The method of FIG. 4B may be performed byserver systems 106, based on information, images, and data received fromphysicians 102 and/or third party providers 104 over electronic network100.

As shown in FIG. 4B, method 420 may include obtaining patient-specificmodels of coronary geometry based on an image of a patient (e.g., CTA).More specifically though, method 420 may involve collecting one or moremodels in order to create or determine models for comparison topatient-specific models undergoing analysis. In one embodiment, themodels may be derived from models associated with individuals, meaningpatients other than the patient associated with the patient-specificmodel undergoing analysis. Aggregating models from a collection ofindividuals may provide indicators or patterns associated with MIoccurrences and/or plaque vulnerability. Method 420 may depict theprocess of a machine-learning algorithm that continually updates andrevises its understanding of indications of plaque vulnerability. Inother words, method 420 may be a process of training a prediction systemusing collected features in order to identify indications of acutemyocardial infarction (MI) likelihood over time (if sufficiently largeMI patient data were used for training) and/or plaque vulnerability orfeatures of vulnerability measured from OCT, IVUS, and near-infraredspectroscopy (if a surrogate plaque vulnerability model was used fortraining). The trained prediction system (e.g., a machine learningsystem) may then be used to test a patient to predict the risk of plaquerupture or myocardial infarction by employing method 400, e.g., byobtaining an image of a patient (e.g., CTA), extractingimage/hemodynamic/biomechanical features and calculating risk factors,and sending predicted risk factors to users (e.g., physicians). Forexample, if the prediction system is trained to predict thevulnerability of one or more locations of one or more locations ofcoronary lesions, the prediction system may compare models within theprediction system against a patient-specific model associated with atarget patient. The comparison may allow the prediction system toestimate vulnerability probabilities for the particular target patient.

In the phase of training a prediction system to assess cardiac risk,training may derive from presence of an MI event associated with alesion, if there exists a sufficiently large number of MI eventpatients. If the number of MI events is limited, a surrogate plaquevulnerability model can be used in place of the actual MI events. Thesurrogate plaque vulnerability model can be utilized from vulnerablefeatures characterized by invasive imaging such as optical coherencetomography (OCT), near infrared spectroscopy (NIRS) and virtualhistology intravascular ultrasound (VH-IVUS). An embodiment of method400 will now be described in detail with reference to an exemplarytraining mode for the prediction system, such as method 420. In oneembodiment, method 420 may begin with determining every location in thevarious patient-specific geometric models for which there is informationabout the plaque vulnerability (step 422).

Exemplary Training Mode

For one or more individuals, acquire a digital representation (e.g., thememory or digital storage [e.g., hard drive, network drive] of acomputational device such as a computer, laptop, DSP, server, etc.) ofthe following items for each time point:

Acquire: a patient-specific model of the geometry for the patient'sascending aorta, coronary artery tree, myocardium, valves, and chambers.

Acquire: patient information comprising, at least, estimates ofphysiological or phenotypic parameters of the patient, including: bloodpressure, hematocrit level, patient age, patient gender, myocardialmass, general risk factors of coronary artery disease, and/or one ormore biomarkers. The myocardial mass may be derived by segmenting themyocardium in the image, calculating the volume in the image, and usingan estimated density of 1.05 g/mL to estimate the myocardial mass.

The general risk factors of coronary artery disease may include:smoking, diabetes, hypertension, lipid level (e.g., low densitylipoprotein (LDL) cholesterol (LDL-C) levels), dietary habits, familyhistory, physical activity, sexual activity, weight (abdominal obesity),cholesterol, and/or stress state (e.g., depression, anxiety, ordistress).

The biomarkers may include: complement reactive protein (CRP),fibrinogen, WBC (White blood cell) count, matrix metalloproteinase(e.g., MMP-9, MMP-3 polymorphism), IL-6, IL-18, and TOT-a (Cytokines),circulating soluble CD40 Ligand (sCD40L), and/or Vascular CalcificationMarkers (e.g., Osteopontin).

Acquire: image features from CT, including: plaque burden (thickness,area, volume), SYNTAX score, napkin ring, and/or necrotic core

Acquire: one or more estimates of biophysical hemodynamic characteristicfrom computational fluid dynamics analysis. Computational fluid dynamicsto simulate blood flow have been well studied. The estimates in thisembodiment include:

Simulation condition (e.g., rest, exercise (Low/Medium/High grade bychanging degree of cardiac output), hyperemia, etc.).

Hemodynamic quantity:

-   -   Max, cyclic wall-shear stress and mean wall-shear stress,        defined as

${{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{\rightarrow}{t_{s}}\ {t}}}}},$

where {right arrow over (t_(s))} is the wall shear stress vector definedas the in-plane component of the surface traction vector.

Turbulent kinetic energy (TKE). This quantity is a measure of theintensity of turbulence associated with eddies in turbulent flow, and ischaracterized by measured root-mean-square velocity fluctuation. TKE canbe normalized by kinetic energy.

Acquire: one or more estimates of vessel wall and plaque biomechanicalcharacteristic from computational solid dynamics analysis. The estimatesin this embodiment may include: simulation condition (pulsatile orsteady flow) (rest, exercise (Low/Medium/High grade by changing degreeof cardiac output), and/or hyperemia; biomechanical material propertiesof vessel wall and plaque derived from literature data and/or imagecharacteristics (e.g., linear elastic, nonlinear elastic, viscoelasticconstitutive models, density, compressible or incompressible materialbehavior, and/or ultimate strength of material; and biomechanical stressand strain (e.g., max or mean cyclic wall and plaque stress, max or meancyclic wall and plaque strain, and/or alternating stress and strain).

Acquire: location(s) of plaque at culprit lesion being targeted forprediction of vulnerability. The location of plaque can be determined byuse of CT and other imaging modalities including intravascularultrasound, or optical coherence tomography.

Step 422 may thus include determining every location in the variouspatient-specific geometric models for which there is information aboutthe plaque vulnerability. Then, step 424 may include creating a featurevector for each location that contains a numerical description ofphysiological or phenotypic parameters of the patient and a descriptionof the local geometry and biophysical hemodynamic characteristic.Specifically the feature vector may contain:

Systolic and Diastolic Blood Pressure

Heart Rate

Blood properties including: plasma, red blood cells (erythrocytes),hematocrit, white blood cells (leukocytes) and platelets (thrombocytes),viscosity, yield stress

Patient age, gender, height, weight

Lifestyle characteristics: presence or absence of currentmedications/drugs

General risk factors of CAD, such as: smoking status, diabetes,hypertension, lipid level (e.g., low density lipoprotein (LDL)cholesterol (LDL-C) levels), dietary habits, family history, physicalactivity, sexual activity, weight (abdominal obesity), cholesterol,and/or stress state (e.g., depression, anxiety or distress)

Biomarkers, such as: complement reactive protein (CRP), fibrinogen, WBC(White blood cell), matrix metalloproteinase (e.g., MMP-9, MMP-3polymorphism), IL-6, IL-18, and TOT-a (Cytokines), circulating solubleCD40 Ligand (sCD40L), vascular calcification markers (e.g.,Osteopontin).

Amount of calcium in aorta and valve

Presence of aortic aneurysm

Presence of valvular heart disease

Presence of peripheral disease

Epicardial fat volume

Cardiac function (ejection fraction)

Characteristics of the aortic geometry, e.g., cross-sectional areaprofile along the ascending and descending aorta, and/or surface areaand volume of the aorta

SYNTAX Score

Characteristics of coronary lesion, e.g., minimum lumen area, minimumlumen diameter, degree of stenosis at lesion (percentage diameter/areastenosis), e.g., by determining virtual reference area profile by usingFourier smoothing or kernel regression, and/or computing percentagestenosis of lesion using the virtual reference area profile along thevessel centerline; location of stenotic lesions, such as by computingthe distance (parametric arc length of centerline) from the main ostiumto the start or center of the lesion; length of stenotic lesions, suchas by computing the proximal and distal locations from the stenoticlesion, where cross-sectional area is recovered; and/or irregularity (orcircularity) of cross-sectional lumen boundary.

Characteristics of coronary lumen intensity at lesion, e.g., based onintensity change along the centerline (slope of linearly-fittedintensity variation)

Characteristics of surface of coronary geometry at lesion, e.g., basedon 3-D surface curvature of geometry (Gaussian, maximum, minimum, mean),e.g., based on characteristics of coronary centerline (topology) atlesion:

-   -   Curvature (bending) of coronary centerline        -   Compute Frenet curvature

${\kappa = \frac{{p^{\prime} \times p^{''}}}{{p^{\prime}}^{3}}},$

where p is coordinate of centerline parameterized by cumulativearc-length to the starting point

-   -   Compute an inverse of the radius of a circumscribed circle along        the centerline points    -   Tortuosity (non-planarity) of coronary centerline        -   Compute Frenet torsion

${\tau = \frac{\left( {p^{\prime} \times p^{''}} \right) \cdot p^{\prime\prime\prime}}{{{p^{\prime} \times p^{''}}}^{2}}},$

where p is coordinate of centerline

Characteristics of coronary deformation (possibly involving multi-phaseCCTA (e.g., diastole and systole)): distensibility of coronary arteryover cardiac cycle; bifurcation angle change over cardiac cycle; and/orcurvature change over cardiac cycle

Characteristics of existing plaque: location of plaque along centerline(distance to closest upstream bifurcation point, and/or bifurcationangle of coronary branches if plaque is located at the bifurcation),adverse plaque characteristics (presence of positive remodeling,presence of low attenuation plaque, and/or presence of spottycalcification), plaque burden (thickness, area, and/or volume), presenceof Napkin ring, intensity of plaque, type of plaque (calcified,non-calcified), distance from the plaque location to ostium (LM or RCA),and/or distance from the plaque location to the nearestdownstream/upstream bifurcation.

Characteristics of coronary hemodynamics derived from computational flowdynamics or invasive measurement: To obtain transient characteristics ofblood, pulsatile flow simulation may be performed by using a lumpedparameter coronary vascular model for downstream vasculatures, inflowboundary condition with coupling a lumped parameter heart model and aclosed loop model to describe the intramyocardial pressure variationresulting from the interactions between the heart and arterial systemduring cardiac cycle.

Measured FFR

Pressure gradient

FFRct

Maximum, cyclic and mean wall-shear stress

Turbulent kinetic energy

Local flow rate

Characteristics of wall and plaque biomechanics derived fromcomputational solid dynamics: plaque mean, max and alternating stressand strain, and/or ultimate stress and strength

Once feature vector creation is completed in step 424, step 426 mayinclude associating the feature vector with available models of plaquevulnerability at the same location. Such models may include surrogatevulnerable feature models. The following surrogate vulnerable featurescan be available at the time when cardiac images were acquired byinvasive imaging such as OCT, NIRS, or VH-IVUS:

Thin cap fibroatheroma (TCFA)<65 microns

Large necrotic core

a. 25% of plaque area

b. >120 degree circumference

c. 2-22 mm long

Speckled pattern of calcification

Macrophages

As part of step 426, the associations created between feature vectorsand models may permit recognition of trends, similarities, and/orgroupings of various factors that may indicate plaque vulnerability orlikelihood or presence of MI events at specific points. In oneembodiment, step 426 may include quantifying the associations as featureweights, such that relationships between various factors that play intocardiac risk can be returned as predictions. In other words, theprediction system may assign or combine feature vectors with weights.Part of the training aspect of the prediction system may includecontinually adjusting feature weights for better accuracy inpredictions. Thus, step 426 may include training a machine-learningalgorithm (e.g. a linear SVM) to learn the associations and/or featureweights in order to predict plaque vulnerability or presence of MI eventat points on a model.

Then for step 428, results (e.g. feature weights) of the machinelearning algorithm-based prediction system may be continually saved as adigital representation (e.g., the memory or digital storage (e.g., harddrive, network drive) of a computational device such as a computer,laptop, DSP, server, etc.). Step 428 may include continually updatingfeature weights as more patient-specific models are collected andfeature vectors constructed. Step 428, therefore, permits a predictionsystem that continually incorporates features input from acquiredpatient-specific models.

Exemplary Application of Prediction System

For a target patient, an exemplary method may include acquiring adigital representation (e.g., the memory or digital storage (e.g., harddrive, network drive) of a patient-specific model of the geometry forthe patient's ascending aorta, coronary artery tree, myocardium, valves,and chambers. This geometry may be represented as a list of points inspace (possibly with a list of neighbors for each point) in which thespace can be mapped to spatial units between points (e.g., millimeters).This model may be derived by performing a cardiac CT imaging of thepatient in the end diastole phase of the cardiac cycle. This image thenmay be segmented manually or automatically to identify voxels belongingto the aorta and the lumen of the coronary arteries. Once the voxels areidentified, the geometric model can be derived (e.g., using marchingcubes). The process for generating the patient-specific model of thegeometry may be the same as in the training mode. A list ofphysiological and phenotypic parameters of the patient may be obtainedduring training mode.

For every point in the patient-specific geometric model, the exemplarymethod may include creating a feature vector for that point including anumerical description of the geometry and biophysical hemodynamic andwall and plaque biomechanical characteristic at that point, andestimates of physiological or phenotypic parameters of the patient.These features may be the same as the quantities used in the trainingmode.

The exemplary method may include using the saved results of themachine-learning algorithm produced in the training mode (e.g., featureweights) to produce estimates of the probability of the plaque ruptureor MI event at lesions in the patient-specific geometric model. Theseestimates may be produced using the same machine learning technique usedin the training mode. The exemplary method may include saving thepredicted probability of the plaque vulnerability (rupture) for lesionsor MI event to a digital representation (e.g., the memory or digitalstorage (e.g., hard drive, network drive) of a computational device suchas a computer, laptop, DSP, server, etc.), and communicating thepatient-specific risk factors to a health care provider.

FIG. 5A is a block diagram of an exemplary method 500 for medicaltherapy planning and lifestyle management. The method of FIG. 5A may beperformed by server systems 106, based on information, images, and datareceived from physicians 102 and/or third party providers 104 overelectronic network 100. In one embodiment, FIG. 5A may be an extensionof the understanding of cardiac risk developed from methods 400 and 420.For instance, method 500 may determine the impact of various medicaltherapies or treatments and/or lifestyle modifications on loweringcardiac risk. More specifically, method 500 may involve determining theeffect of medical therapies or lifestyle modifications on the featuresused in the cardiac risk predictions. As shown in FIG. 5A, method 500may first include, retrieving features used in method 400 to predictcardiac risk prediction (step 502). For step 504, various medicaltherapy, protocols, and/or lifestyle modifications may be determined.For instance, medical therapies may include anti-ischemic drugs forischemia management, antiplatelet agents, and/or lipid-lowering agentsfor event prevention, etc. Anti-ischemic drugs may include nitrates,beta-blockers (e.g., metopropl, bisoprolol, antenolol, etc.),ivabradine, etc. Exemplary antiplatelet agents may include low-doseaspirin, while lipid-lowering agents may include statin treatments.Lifestyle modifications may include: smoking cessation, diet control,physical and/or sexual activity, weight management, arterialhypertension management, and stress management.

Step 506 may include determining the effect of a given medical therapy,protocol, or lifestyle modification on the features used in computedplaque vulnerability prediction. For example, effects for lifestylemodifications and control of risk factors may be as follows:

Smoking cessation: can reduce systolic pressure by 3.5+/−1.1 mmHg anddiastolic pressure by 1.9+/−0.7 mmHg and reduce heart rate by 7.3+/−1.0beats/min [18].

Diet control: N-3 polyunsaturated fatty acid (PUFA) consumption (e.g.,from oily fish) can reduce triglycerides; and decreased triglycerideslevel can reduce blood viscosity by 2%.

Physical activity: regular physical activity can reduce blood pressureby 3 mmHg; regular physical activity can cause plaque regression.

Sexual activity: sexual activity is associated with 75% of exerciseworkload in systolic BP; regular sexual activity can reduce bloodpressure by 2 mmHg.

Weight management: weight reduction in obese people can decrease BP by10% and reduce blood viscosity by 2%.

Arterial hypertension management: reductions in blood pressure of 10-12mmHg systolic and 5-6 mmHg diastolic can decrease coronary arterydisease of 16%.

Stress management: relief of depression, anxiety, and distress canreduce symptoms resulting in 10% HR and blood pressure reduction.

Effects for anti-ischemic drugs for ischemia management may include:

Nitrates: 5% increase in diameter of epicardial coronary arteries forsublingual nitroglycerin (GTN) capsules and 13% increase in diameter ofepicardial coronary arteries for isosorbide dinitrate (ISDN).

Beta-blockers (e.g., metoprolol, bisoprolol, atenolol): reduction ofheart rate by 10%; Reduction of blood pressure by 10%.

Ivabradine: reduction of heart rate by 8.1+/−11.6 beats/min

Effects associated with antiplatelet agents for event prevention may be:low-dose aspirin; reduce blood pressure by 20 mmHg

Impact of lipid-lowering agents for event prevention may include: statintreatment reduces low density lipoprotein (LDL) cholesterol (LDL-C)levels and thus decrease blood viscosity by 2%.

Step 506 may include determining the effects on features (e.g. from orrelating to feature vectors) for a target patient (based a respectivepatient-specific model). Method 500 may thus determine the effect of agiven medical therapy protocol or lifestyle modification on the featuresused in computed plaque vulnerability prediction (step 506). Method 500may further include providing an optimal treatment protocol to aphysician based on the effect of one or more treatment protocols on therisk factor prediction (step 508). In one embodiment, step 508 mayoptionally include producing a rendering of the effects of varioustreatment protocols such that a physician may compare protocols andprojections of effects on the features based on the protocols. A furtherembodiment of step 508 may include analyzing the combined effects ofmultiple treatment protocols and/or lifestyle modifications such thatphysicians may offer a treatment regimen that may include more than oneform of therapy.

FIG. 5B is a block diagram of an exemplary method 520 by which a machinelearning algorithm may determine effects of various medical treatmentsand/or lifestyle modifications on the features. The method of FIG. 5Bmay be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 100. Essentially, method 520 describes oneembodiment of step 506 of method 500 in more detail. In one embodiment,method 500 for guiding medical therapy may use a machine-learning basedcardiac risk predictor established in the method of FIG. 4B and add anadditional layer of machine-based learning by evaluatingpatient-specific cardiac imaging models through medical therapy andlifestyle modifications. Therefore, method 520 may help predict, forinstance, the probability of plaque rupture risk using updated featuresand a trained machine-learning algorithm.

For example, method 520 may include employing patient-specific modelsreflecting the geometry of the patient-specific model used in method 420of training the cardiac risk prediction system, including the list ofphysiological and phenotypic parameters of the patient (e.g., obtainedduring training mode for the cardiac event predictor). In other words,patient-specific models used in method 520 may include geometry ofascending aortas, coronary artery trees, myocardium, valves, andchambers respective to each patient.

For every point in each patient-specific geometric model, method 520 mayinclude feature vectors for each point, comprising a numericaldescription of the geometry and biophysical hemodynamic andbiomechanical characteristic at that point, and estimates ofphysiological or phenotypic parameters of the patient. These featuresmay be the same as the quantities used in the training mode for thecardiac risk prediction system.

Step 522 may include virtually adjusting feature sets to simulateapplication of medical therapies or lifestyle modifications topatient-specific models. Then for step 524, method 520 may estimateprobability of cardiac risk according to the adjustments. In oneembodiment, step 524 may rely on the saved results of themachine-learning algorithm produced in the training mode (e.g., featureweights) to produce estimates of the probability. These estimates may beproduced using the same machine-learning algorithm used in the trainingmode for the cardiac event predictor. For example, if beta-blocker(e.g., metoprolol, bisoprolol, atenolol) is chosen for a medicaltherapy, the algorithm may update the following features: reduce bloodpressure by 10% and heart rate by 10% and/or update boundary conditionsfor coronary blood flow simulation and extract new hemodynamics and walland plaque biomechanical features.

Based on the estimates, step 526 may include a comparison of estimatesfor various applied protocols and modifications. In one embodiment, step526 may include a second machine-learning algorithm specifically appliedto the effects of treatment given various combinations of featuresand/or feature vectors. For example, this second machine-learningalgorithm may be an extension of the first machine-learning algorithmfor cardiac risk. In another instance, the second machine-learningalgorithm may be a separate, independent entity. In such a case, themodels on which the machine learning algorithms are constructed may beindependent and/or overlap.

Step 528 may include determining an optimal treatment and/or lifestylemodification based on the comparison from step 526. Optimal treatmentsmay be based simply on the effects of optimal treatments and/orlifestyle modifications on features. In a further embodiment, theoptimal treatments may take into account patient-specific factors. Forinstance, step 528 may include determining a patient's geographicallocation and determining optimal treatment in light of the location. Forexample, a patient that lives near a beach may have an optimal lifestylemodification involving swimming whereas such a recommendation may beless optimal for a land-locked patient. The optimal treatments mayfurther consider other patient treatments. For example, running orwalking may be a lifestyle modification that best suits a patient basedon the effects of the modification on a patient's factors. However, itmay not be practical for a patient with a recent knee injury to employsuch a modification. Step 528 may thus create an optimal treatment, withrespect to a patient's specific conditions. Step 528 may further includesaving the predicted probability of the plaque vulnerability (rupture)for lesions to a digital representation (e.g., the memory or digitalstorage (e.g., hard drive, network drive) of a computational device suchas a computer, laptop, DSP, server, etc.) for a given medical therapy.In relation to step 508 of method 500, step 508 may include outputtingto a doctor the effect of one or more treatment protocols on the riskfactor prediction and suggesting optimal treatment protocol based on thepredicted plaque vulnerability determined in step 528.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A computer-implemented method of determining aneffect of a treatment on coronary plaque vulnerability, the methodcomprising: acquiring anatomical image data of at least part of apatient's vascular system; performing, using a processor, one or more ofimage characteristics analysis, geometrical analysis, computationalfluid dynamics analysis, and structural mechanics analysis on theanatomical image data; predicting, using the processor, a coronaryplaque vulnerability present in the patient's vascular system based onresults of one or more of the image characteristics analysis,geometrical analysis, computational fluid dynamics analysis, andstructural mechanics analysis of the anatomical image data; modifyingone or more of the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image data based on a proposed treatment; anddetermining an effect of the treatment on the coronary plaquevulnerability based on the modified one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage.
 2. The method of claim 1, further including: acquiring, for eachof a plurality of individuals, individual-specific anatomic data andblood flow characteristics of at least part of the individual's vascularsystem; and predicting risk of plaque rupture or myocardial infarctionbased on the individual-specific anatomic data and blood flowcharacteristics for each of the plurality of individuals, wherein thedetermining of the effect of the treatment is based on the risk ofplaque rupture or myocardial infarction.
 3. The method of claim 1,further including: generating a patient-specific geometric model of atleast part of the patient's vascular system; extracting, using aprocessor, geometrical features, image features, hemodynamiccharacteristics of blood flow through the patient-specific geometricmodel, and/or biomechanical features associated with the geometricmodel; and predicting, using the processor, cardiac risk, based onresults of the extraction of geometrical features, image features,hemodynamic characteristics of blood flow through the patient-specificgeometric model, and/or biomechanical features associated with thegeometric model.
 4. The method of claim 3, further including: acquiringone or more physiological and/or phenotypic parameters; obtaining one ormore geometric qualities of one or more coronary arteries of thepatient-specific geometric model of the patient's vascular system; anddetermining presence or absence of plaque vulnerability at each of aplurality of locations in the patient-specific geometric model of thepatient's vascular system.
 5. The method of claim 4, further including:generating one or more patient feature vectors based on the geometricalfeatures, image features, the hemodynamic characteristics, thebiomechanical features, and/or the one or more physiological and/orphenotypic parameters, wherein the one or more patient feature vectorsare associated with each of the plurality of locations and/or each ofthe plurality of locations associated with the determined presence ofplaque vulnerability.
 6. The method of claim 5, further including:determining one or more treatments; and determining an associationbetween the one or more patient feature vectors, the one or moretreatments, one or more known indicators of cardiac risk, or acombination thereof, wherein the determining of the effect of thetreatment is based on the association between the one or more patientfeature vectors, the one or more treatments, one or more knownindicators of cardiac risk, or a combination thereof.
 7. The method ofclaim 6, further including: adjusting one or more feature setsassociated with the one or more patient feature vectors, wherein thedetermining of the effect of the treatment on the coronary plaquevulnerability is further based on the step of adjusting one or morefeature sets associated with the one or more patient feature vectors. 8.The method of claim 6, further including: generating a presentation ofat least a portion of the one or more treatments or the effect of thetreatment for each treatment of the portion of the one or moretreatments.
 9. A system of determining an effect of a treatment oncoronary plaque vulnerability, the system comprising: a data storagedevice storing instructions for predicting coronary plaque vulnerabilityfrom patient-specific anatomic image data; and a processor configured toexecute the instructions to perform a method including: acquiringanatomical image data of at least part of the patient's vascular system;performing, using a processor, one or more of image characteristicsanalysis, geometrical analysis, computational fluid dynamics analysis,and structural mechanics analysis on the anatomical image data;predicting, using the processor, a coronary plaque vulnerability presentin the patient's vascular system based on results of one or more of theimage characteristics analysis, geometrical analysis, computationalfluid dynamics analysis, and structural mechanics analysis of theanatomical image data; modifying one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage data based on a proposed treatment; and determining an effect ofthe treatment on the coronary plaque vulnerability based on the modifiedone or more of the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image.
 10. The system of claim 9, wherein theprocessor is further configured for: acquiring, for each of a pluralityof individuals, individual-specific anatomic data and blood flowcharacteristics of at least part of the individual's vascular system;and predicting risk of plaque rupture or myocardial infarction based onthe individual-specific anatomic data and blood flow characteristics foreach of the plurality of individuals, wherein the determining of theeffect of the treatment is based on the risk of plaque rupture ormyocardial infarction.
 11. The system of claim 9, wherein the processoris further configured for: generating a patient-specific geometric modelof at least part of the patient's vascular system; extracting, using aprocessor, geometrical features, image features, hemodynamiccharacteristics of blood flow through the patient-specific geometricmodel, and/or biomechanical features associated with the geometricmodel; and predicting, using the processor, cardiac risk, based onresults of the extraction of geometrical features, image features,hemodynamic characteristics of blood flow through the patient-specificgeometric model, and/or biomechanical features associated with thegeometric model.
 12. The system of claim 11, wherein the processor isfurther configured for: acquiring one or more physiological and/orphenotypic parameters; obtaining one or more geometric qualities of oneor more coronary arteries of the patient-specific geometric model of thepatient's vascular system; and determining a presence or absence ofplaque vulnerability at each of a plurality of locations in thepatient-specific geometric model of the patient's vascular system. 13.The system of claim 12, wherein the processor is further configured for:generating one or more patient feature vectors based on the geometricalfeatures, image features, the hemodynamic characteristics, thebiomechanical features, and/or the one or more physiological and/orphenotypic parameters, wherein the one or more patient feature vectorsare associated with each of the plurality of locations and/or each ofthe plurality of locations associated with the determined presence ofplaque vulnerability.
 14. The system of claim 13, wherein the processoris further configured for: determining one or more treatments; anddetermining an association between the one or more patient featurevectors, the one or more treatments, one or more known indicators ofcardiac risk, or a combination thereof, wherein the determining of theeffect of the treatment is based on the association between the one ormore patient feature vectors, the one or more treatments, one or moreknown indicators of cardiac risk, or a combination thereof.
 15. Thesystem of claim 14, wherein the processor is further configured for:adjusting one or more feature sets associated with the one or morepatient feature vectors, wherein the determining of the effect of thetreatment on the coronary plaque vulnerability is further based on thestep of adjusting one or more feature sets associated with the one ormore patient feature vectors.
 16. The system of claim 14, wherein theprocessor is further configured for: generating a presentation of atleast a portion of the one or more treatments or the effect of thetreatment for each treatment of the portion of the one or moretreatments.
 17. A non-transitory computer readable medium for use on acomputer system containing computer-executable programming instructionsfor performing a method of determining the effect of a treatment oncoronary plaque vulnerability, the method comprising: acquiringanatomical image data of at least part of the patient's vascular system;performing, using a processor, one or more of image characteristicsanalysis, geometrical analysis, computational fluid dynamics analysis,and structural mechanics analysis on the anatomical image data;predicting, using the processor, a coronary plaque vulnerability presentin the patient's vascular system based on results of one or more of theimage characteristics analysis, geometrical analysis, computationalfluid dynamics analysis, and structural mechanics analysis of theanatomical image data; modifying one or more of the imagecharacteristics analysis, geometrical analysis, computational fluiddynamics analysis, and structural mechanics analysis on the anatomicalimage data based on a proposed treatment; and determining an effect ofthe treatment on the coronary plaque vulnerability based on the modifiedone or more of the image characteristics analysis, geometrical analysis,computational fluid dynamics analysis, and structural mechanics analysison the anatomical image.
 18. The non-transitory computer readable mediumof claim 17, the method further comprising: acquiring, for each of aplurality of individuals, individual-specific anatomic data and bloodflow characteristics of at least part of the individual's vascularsystem; and predicting risk of plaque rupture or myocardial infarctionbased on the individual-specific anatomic data and blood flowcharacteristics for each of the plurality of individuals, wherein thedetermining of the effect of the treatment is based on the risk ofplaque rupture or myocardial infarction.
 19. The non-transitory computerreadable medium of claim 17, the method further comprising: generating apatient-specific geometric model of at least part of the patient'svascular system; extracting, using a processor, geometrical features,image features, hemodynamic characteristics of blood flow through thepatient-specific geometric model, and/or biomechanical featuresassociated with the geometric model; and predicting, using theprocessor, cardiac risk, based on results of the extraction ofgeometrical features, image features, hemodynamic characteristics ofblood flow through the patient-specific geometric model, and/orbiomechanical features associated with the geometric model.
 20. Thenon-transitory computer readable medium of claim 19, the method furthercomprising: acquiring one or more physiological and/or phenotypicparameters; obtaining one or more geometric qualities of one or morecoronary arteries of the patient-specific geometric model of thepatient's vascular system; and determining a presence or absence ofplaque vulnerability at each of a plurality of locations in thepatient-specific geometric model of the patient's vascular system.